Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries

Energy Strategy Reviews 29 (2020) 100484
Available online 29 March 2020
2211-467X/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Renewable energy, institutional stability, environment and economic
growth nexus of D-8 countries
Mahjabeen a,*
, Syed Z.A. Shah a
, Sumayya Chughtai a
, Biagio Simonetti b,c,d
a
International Islamic University, Islamabad, Pakistan
b
University of Sannio, Benevento, Italy
c
WSB University, Gdansk, Poland
d
National Institute of Geophysics and Volcanology (INGV), Naples, Italy
A R T I C L E I N F O
Keywords:
Renewable energy
Non-renewable energy
Institutional quality
Economic output
Environment quality
D-8 countries
A B S T R A C T
The anthropogenic impact of conventional energy sources encourages the utilization of renewable energy, as it
has become a strategic commodity for economic growth. On the other hand, institutional stability is the pre-
requisite without which environmental quality cannot be assured and the economy cannot function. However,
in recent literature, very little consideration has been given to this important phenomenon. This study is set to
analyze the energy-institutional stability-economic growth nexus, as well as the energy-institutional stability-
environmental quality nexus, by incorporating the Cobb Douglas production function and the Diet and Rosa
environmental function respectively. The sample consists of the D-8 countries and the time period spans 1990 to
2016. To analyze the developed models, Autoregressive Distributive Lag (ARDL), Fully Modified Ordinary Least
Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) tests are applied, along with other econometric
techniques. The panel ARDL statistics indicate significant cointegration among all variables of both functions,
while the FMOLS test reveals that consumption of both nonrenewable and renewable energy has a positive
impact on economic growth, as well as on environmental degradation. Further, results indicate that institutional
stability is crucial for establishing a nation on a sound footing and protecting environmental quality. Based on
these results, the study suggests a blend of both types of energy and a gradual transition toward renewable
energy sources, with better implementation of policies and technological advances, to produce, preserve, and
transmit renewable energy production.
1. Introduction
Environmental degradation and greenhouse gas emissions are the
new challenges for the new millennium. Rapid economic growth poses a
serious threat to mankind, in the form of resource depletion, pollution,
and climate change. To deal with these challenges, the UNFCCC (United
Nations Framework Convention on Climate Change) has established a
basic framework for global cooperation on this very issue; in addition,
the 2030 Agenda, Kyoto Protocols (1997) and Copenhagen Accords
(2009) are deterministic steps to deal with the issue of deteriorating
environmental quality. Energy consumption is the pre-requisite of eco
nomic growth and considered the fundamental aspect of economic
growth. Around the world, the dominant source of energy is fossil fuels,
also called conventional energy sources. Almost 85% of cumulative
energy needs are fulfilled by fossil fuels, which also account for 56.6% of
the emissions of greenhouse gasses (CO2 equivalent) [1]. In the coming
era of economic and environmental compatibility, the anthropogenic
emission of greenhouse gases should be reduced to overcome and
mitigate catastrophic environmental degradation and climate change
[2]. Therefore, a transition to cleaner energy is the ultimate challenge
for economies, to ensure energy security, reduce environmental degra
dation, and spur economic growth [3–6].
Renewable energy is considered a strategic commodity and one of
the basic indicators of economic growth and sustainable development
[7]. Renewable energy sources are mainly solar (photovoltaic), wind,
tidal, waste and biomass, which are considered eco-friendly and
cost-effective, as they reduce harmful climate change, mitigate pollu
tion, provide energy security, and help alleviate poverty by providing
electricity to remote areas due to their decentralized nature [8–10]. A
wide range of studies have analyzed the impact of renewable and
* Corresponding author.
E-mail address: Mahjabeen_88@hotmail.com (Mahjabeen).
Contents lists available at ScienceDirect
Energy Strategy Reviews
journal homepage: http://www.elsevier.com/locate/esr
https://doi.org/10.1016/j.esr.2020.100484
Received 4 May 2019; Received in revised form 27 February 2020; Accepted 18 March 2020

2
non-renewable energy on economic output and carbon emissions; most
of these studies have shown a negative association of renewable energy
sources with carbon emissions [11–13] and a positive association with
economic output [13–15] but, still, the findings on the potential of
renewable energy to reduce environmental pollution and enhance sus
tainable development are mixed [12]. Some studies also demonstrate no
differential effect of renewable and nonrenewable energy production
and consumption on carbon emissions [16–18]. Similarly, research
shows that renewable energy has a positive association with the envi
ronment when it reaches a certain minimum threshold level. The
negative effects of renewable energy on environmental quality are
mostly caused by a lack of technology, storage quality, as well as poor
transmission systems [19]. Therefore, to increase sustainable develop
ment, the negative environmental impact of renewable energy con
sumption can be controlled by technological advances and proper
transmission systems [9,20–22].
Developing countries face multifaceted energy challenges. They are
expanding their economic activities with a parallel increase in demand
for energy that is mostly met by conventional sources. These conven
tional energy sources (gas, coal, oil, etc.) are being depleted gradually
and their prices are very volatile. As developing countries are net im
porters of these conventional sources, they are affected adversely by
price fluctuations. These countries are also characterized by low insti
tutional quality and long procedural delays, as well as unpredictable
investment needs due to corruption and hierarchical interference [23].
Governmental responsiveness to the population’s concern is the main
contributing factor that can lead a nation to economic and social
development, but developing countries have low political stability and
poor democratic quality. Research indicates that economic growth is
higher in democratic states than autocratic states [14] because, in a
high-quality political regime, energy consumption has a greater impact
on economic growth [24], and the effect different macroeconomic fac
tors have on environmental quality depends on institutional quality
[25]. But the phenomenon of institutional stability in the context of
environmental quality and economic growth is still understudied and
needs to be further explored [14,24].
Based on the above explanations, this study’s sample comprises the
D-8 or developing eight countries, to analyze how different economic
factors, specifically renewable and nonrenewable energy consumption,
institutional stability, and other macroeconomic factors, affect eco
nomic growth and environmental quality in these countries. To analyze
the energy-institutional stability-economic growth nexus, the
Cobb–Douglas production function is applied to avoid the errors of
omitted variables and model specification [26,27]. The Cobb–Douglas
production function is widely used to express the relation of output to
input [13,28] and considered as the modern perspective of economic
growth [26]. This study incorporates economic activity as the output of
various production factors: renewable and nonrenewable energy con
sumption, capital, labor, and institutional stability. Further, to analyze
the energy-institutional stability-environment nexus, Diet and Rosa’s
environmental function [29] is incorporated. This model particularly
measures the effect of population, affluence and environmentally
damaging technology and has been further modified and adopted by
many researchers [30]. This study has adopted the modified version of
the model [13] to analyze the impact of renewable and nonrenewable
energy consumption, affluence, population, and institutional stability on
environmental quality.
This study makes significant contributions to the literature: first,
though there is an expanding literature on the energy-economic-
environmental nexus, D-8 countries have rarely been targeted before.
Empirical findings can vary substantially among different groups of
countries, even for the same country, due to the different economic
situations and chosen time periods [7,27]. So, this study will increase
understanding of the sampled countries’ economies and environmental
conditions. Secondly, by employing the Cobb–Douglas production
function and Diet and Rosa’s environmental function, the study also
makes use of traditional factors, along with some very important input
factors, e.g. democracy index as an indicator of institutional stability.
Political and institutional quality plays a significant role in improving
environmental quality and enhancing economic growth [12,31]. We
argue that sustainable development is associated with institutional sta
bility, but only a few studies have highlighted its impact on sustainable
development [26]. So, this study highlights the role of institutions as the
main input factor for environmental well-being and economic output.
Thirdly, this study adds to the ongoing debate on the usability of
renewable and nonrenewable energy sources, by presenting a holistic
view of the possible impact of explanatory variables on both economic
activity and the environment. Lastly, the study has employed different
econometric techniques of panel data, e.g. FMOLS, DOLS, ARDL, etc.,
and hence offers useful policy implications for the D-8 countries,
considering the economic situation and inferential results.
The next section describes the D-8 countries, followed by a literature
review, specifically on the association of renewable energy, economic
growth, and the environment. The following section contains the data
sources and methodology, followed by results, discussion, conclusion,
limitations, and policy implications.
1.1. Insight into the Developing-8 countries’ energy positions
More than two decades ago, in 1997, eight Muslim countries
(Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan, and
Turkey), also called the D-8 (Developing 8), made a multilateral
agreement with special emphasis on economic cooperation. The mem
ber countries are considered the fastest growing countries and have huge
potential for economic development, thus their consumption of energy
and electricity is also growing. The per capita electricity consumption of
the D-8 countries is 1031 kWh, which is well below the other non-OIC
(Organization of Islamic Cooperation) countries, that have an average
consumption of 2202 kWh per capita [32]. Only Pakistan and Turkey
have a 30% share of renewable energy in their total energy production,
while the rest of the countries produce over 90% of their energy from
fossil fuel sources. Besides this, only Iran and Pakistan possess the ability
to produce energy from nuclear sources [32] and all other member
countries rely mainly on conventional sources of energy production. The
working committee on renewable energy1
(WCRE) has been established
for proper planning and implementation of strategies to spread aware
ness and to take measures to increase the share of renewable energy
sources. Fig. 1 presents energy production and consumption in the D-8
countries, showing their high reliance on conventional energy sources.
These countries are best suited for analysis, as they are all facing the
above-mentioned problems of conventional energy usage, growing
economies and uneven institutional quality.
2. Literature review
Production processes and modernization depend heavily on energy
because sustainable economic growth cannot be achieved without an
uninterrupted energy supply. Renewable energy sources are considered
the best alternative to conventional sources, while a hybrid energy
system is considered to be the ultimate solution, especially for rural
electrification [28]. Considering the importance of renewable energy,
many authors have endeavored to discover its impact on economic ac
tives which can be classified into four types of conclusion or hypothesis.
Since the pioneering study by Kraft and Kraft [33], the
energy-economic nexus has been broadly studied by researchers.
Generally, the association of energy consumption and economic growth
fall into four types of hypotheses, each of which posits implications for
policy-making [13,26,27]. The first one is the growth hypothesis that
subsumes unidirectional causality from energy towards economic
1
http://developing8.org/areas-of-cooperation/renewable-energy/.
Mahjabeen et al.

3
activities. It means the higher the energy consumption, the higher the
economic growth, hence, energy-saving measures and policies can have
contradictory effects on economic activities. The second one is the
feedback hypothesis, which is valid when there is a bidirectional rela
tionship between energy consumption and economic growth. It means
that economic growth and energy consumption affect each other and
have a mutual association. The third hypothesis is the conservation
hypothesis that asserts unidirectional causality from economic growth
towards energy consumption. This hypothesis prevails when economic
growth has a positive impact on energy consumption, while energy
conservation measures and policies do not affect GDP. The last hy
pothesis is the neutrality hypothesis that implies no causal association
between energy consumption and economic growth; so, the results of
previous studies comprise these findings and have found evidence of all
these hypotheses. Below we will discuss the literature, specifically on
renewable energy consumption and economic growth, and, in the next
subsection, studies on renewable energy and the environment are
reviewed.
2.1. Renewable energy consumption, economic growth and institutional
stability
One of the earliest studies on the causality of renewable energy and
economic growth was carried out by Chein and Hu [34], when they
asserted that increased renewable energy has a positive association with
economic efficiency, while increased consumption of conventional en
ergy sources leads to decreased overall economic efficiency. In a recent
study, Paramati, Sinha and Dogan [13] analyzed the role of renewable
energy on economic activates and CO2 emissions for the developing
countries; using data from 19901 to 2012, they concluded that renew
able energy has a positive and significant association with economic
growth, while having a negative and adverse effect on CO2 emissions,
hence they advocated employing more renewable energy in the current
grid system to pace economic growth and to save the environment from
degradation. Similarly, the same notion is adopted by Ref. [26], where
the authors studied the impact of renewable energy consumption on
GDP for Turkey for the period from 1990 to 2015. They found renewable
energy did not affect GDP and asserted that the proportion of renewable
energy was too small to have a significant impact on GDP. They further
suggested increasing the share of renewable energy to decrease the
deficit of energy trade. Contrary to these findings, another study
analyzed the same notion for the same country and found a unidirec
tional causality between renewable energy and economic growth, while
an autoregressive distributive lag (ARDL) test showed the negative as
sociation between renewable energy and economic growth [27].
Another important study on the association between renewable energy
and economic development was conducted using quarterly data from
1972 to 2011. It demonstrated that renewable energy has a feedback
effect with economic growth, while the rest of the variables have a
long-term association with each other [28]. While analyzing the energy
scenario of Pakistan, another study found that energy consumption and
economic growth have bidirectional causality; at the same time, energy
consumption leads to environmental degradation [20]. So a sufficient
proportion of energy sources should be renewable to enable smooth
economic growth without compromising the environment. Apergis and
Payne [15] analyzed the relationship between renewable energy con
sumption and economic growth in the countries of Eurasia from 1992 to
2007. The result of the heterogeneous panel co-integration test reveals
the long-term equilibrium between real GDP, capital formation, labor,
and renewable energy consumption. The error correction model indi
cated a bidirectional association between economic growth and
renewable energy consumption for the short and long-term periods.
Most developing countries are rich in biogases, which are an important
component of renewable energy. Developing countries can enhance the
installed capacity of biogases to optimize their potential contribution in
the energy mix [35]. In another study [7], of the Middle East and North
Africa (MENA), net oil importing countries, results show a bi-directional
causality between both kinds of energy source and economic growth, as
well as between renewable and nonrenewable energy consumption that
highlights their substitutability and interdependency. The results indi
cate that both types of energy source are essential for economic growth.
Along with energy sources, the study also incorporated the regime type
to analyze its impact on economic development for the of Sub-Saharan
Africa for the period of 1980–2012 and found a long-term association
between variables. Specifically, both sources are found to be significant
for economic development, while nonrenewable energy is more salient
for economic growth. Importantly, it was found that autocratic countries
have less economic growth than democratic states.
The role of political stability, regime type, and democracy cannot be
ignored in the energy-economic growth nexus. Using data from 16 Sub-
Saharan countries, Adams et al. [24] analyzed the role of regime type
and energy consumption along with trade openness on economic
growth. Their results confirm that democracy moderates the relation
ship between energy consumption and economic growth. Adams et al.
[14] analyzed the impact of renewable and non-renewable energy
regime type on economic growth for 30 Sub-Saharan countries for the
period of 1980–2012. The results of various tests indicate a long-term
association among variables and that the growth rate of democratic
countries is higher than in autocratic countries. Therefore, most of the
previous studies have been done in bits and pieces, and the role of
institutional stability in economic growth is understudied. The present
study addresses the issue and applies the production function to analyze
the energy-institutional stability-economic growth nexus.
Fig. 1. Energy production by source (top) and consumption (bottom) of the D-8 countries (source: D8 economic outlook 2016-17).
Mahjabeen et al.

4
2.2. Renewable energy consumption, environmental quality and
institutional stability
Renewable energy has a significant role in controlling environmental
degradation and energy transition [36]. The causality between energy
consumption and environmental degradation has been the subject of
intensive studies for the last two decades, with particular emphasis on
renewable energy. The literature covers a wide range of geographical
locations, a number of explanatory variables and a variety of econo
metric tools. In the next section, we will discuss these studies.
One of the earlier studies on the association between renewable
energy and environmental degradation was conducted by Sadorsky
[37], who analyzed the causal relationship for 18 emerging countries for
the period from 1994 to 2003, conducting Granger causality and
Padroni co-integration tests. The results supported the neutrality hy
pothesis in the short term and conservation hypothesis in the long run.
In another study by Apergis and Payne [38], determinants of renewable
energy for the 25 OECD (Organization of Economic Cooperation and
Development) countries for the time span of 1980–2011 were analyzed.
Results of the co-integration and error correction model tests showed the
long-term relationships between renewable energy consumption, CO2
emissions and the rest of the variables, while they also found the evi
dence of feedback hypothesis. Similarly, the association between
renewable energy, environmental degradation and economic growth
has been analyzed for the period of 1975–2008 in 12 MENA countries
[39]. Applying the panel co-integration test, results showed evidence of
the conservation hypothesis in the long run and the growth hypothesis in
the short term. By adding trade openness to the explanatory variables of
the production function, the association between economic output,
renewable energy and CO2 emissions for the BRICS (Brazil, Russia,
India, China and South Africa) countries for the period of 1971–2010
was analyzed [40]. The results of the bound ARDL test reveal the con
servation hypothesis for India and South Africa. The Environmental
Kuznets Curve hypothesis was examined by incorporating renewable
and nonrenewable energy consumption as explanatory variables for the
time span of 1970–2012 [41]. The result of this study confirmed bidi
rectional causality between renewable energy consumption and CO2
emissions, as well as non-renewable energy consumption and CO2
emissions. Further, the researchers found a significant negative associ
ation between renewable energy consumption and CO2 emissions and a
positive association between non-renewable energy consumption and
environmental degradation. In another study [42], the SAARC (South
Asian Association of Regional Cooperation) countries were analyzed to
find possible associations among renewable energy consumption,
poverty, GDP and natural resource depletion. The results of the FMOLS
(Fully Modified Ordinary Least Square) approach showed a significant
association, while the causality test confirms the growth hypothesis
between variables.
According to Sarkodie and Adams [12], institutional quality plays a
significant role in maintaining environmental quality and energy regu
lations. For their regional study of South Africa, they employed ARDL
with a structural break cumulative sum test and results reveal that the
Environmental Kuznets Curve hypothesis holds in South Africa and that
diversification of the energy portfolio through renewable energy is
required to reduce air pollution and vulnerability to energy price
changes. In the energy-economic-environment nexus, Bekun, Alola and
Sarkodie [43] added the natural resource rent and results of the pooled
mean group ARDL suggest a positive association between resource rent
and carbon emissions in the long run. The causality analysis depicts a
feedback mechanism between economic growth, renewable and
nonrenewable energy. Dong, Sun and Hochman [44] advocate the
consumption of natural gas and renewable energy for environmental
sustainability and their analysis confirms the Environmental Kuznets
Curve hypothesis for the BRICS countries. Further, the causality analysis
explains the feedback mechanism among natural gas, renewable energy
consumption, and economic growth. By employing the latest method
ology and allowing for cross-section dependence and slope heteroge
neity, key impact factors for carbon emission are analyzed by Ref. [45]
by including a large sample of 128 countries; overall results indicate a
negative association of renewable energy and carbon emission. Con
trolling for the effect of the ecological footprint, Saint Akadiri, Bekun
and Sarkodie [46] analyzed the impact of energy consumption and GDP
per capita on environmental quality in South Africa. Results indicate
that pollution and environmental degradation is not output-driven but
depends on energy consumption and production. Using an unbalanced
panel of 128 countries, Dong et al. [11] analyzed the impact of renew
able energy, population and economic growth on CO2 emissions.
Considering the slope heterogeneity and cross-sectional dependence, the
authors employed the common correlated effect mean group, whose
results indicate that population and economic growth have a significant
positive influence on carbon emissions, both at a global and regional
level. Similarly, a study by Sarkodie and Strezov [47] shows that the
Environmental Kuznets Curve hypothesis holds in Australia. Proposing a
transition from energy-intensive industries to less carbon-intensive in
dustries, the results of DOLS (Dynamic Ordinary Least Square) and
FMOLS indicate that disaggregated energy sources, along with energy
imports and exports, have a positive influence on the ecological foot
print and carbon emissions in Australia.
Contrary to the above findings, some studies, however, show that
there is no differential effect of renewable and nonrenewable energy on
carbon emission and pollution. The study by Farhani and Shahbaz [16]
analyzed the impact of renewable and nonrenewable energy consump
tion on CO2 emissions for MENA countries. The results of DOLS and
FMOLS show that both kinds of energy contribute to CO2 emissions.
Similarly, the study by Bilgili, Koçak and Bulut [18] researches the same
concept by analyzing the 17 OECD countries for the period from 1997 to
2010. Another study [17] analyzed data from 16 European countries for
the period 1990–2008. The results of ordinary least square (OLS) and
fixed effect reveal that both kinds of energy contribute to greenhouse gas
emissions; however, renewable energy contributes less than nonre
newable energy. Further, the authors state that renewable energy has a
positive association with the environment when it reaches a certain
minimum threshold. The reason for the contribution to environmental
degradation is the lack of production and storage quality and technology
Table 1
Data sources and description.
Code Variable name source
CO2E Carbon Dioxide Emission ¼ Metric Tons Per Capita WDI, EDGAR
Y (GDP) Gross Domestic Product Per Capita WDI
IS Institutional stability ¼ average for political rights and civil liberty (1–7) Freedom House
NREC Non-renewable energy consumption ¼ kilo ton of oil equivalent WDI
REC Renewable energy consumption ¼ % share of renewable energy in total energy consumption WDI
K Capital formation ¼ gross fixed capital formation % of GDP WDI
L Labor ¼ total employment from the age of 15þ WDI
PI Per capita income WDI
POPG Population growth annually WDI
Note: WDI¼ World Development Indicator, EDGAR ¼ Emission Database on Global Atmosphere.
Mahjabeen et al.

5
as well as poor transmission systems [19]. So the negative effect of
renewable energy can be controlled by proper management and trans
mission technology in the grid system.
The role of institutions and economic freedom is taking a stance in
resource management literature. Al-Mulali and Ozturk [48] analyzed
the influence of political stability on environmental quality, along with
energy consumption, trade openness, urbanization and the ecological
footprint for 14 MENA countries. By employing FMOLS, the authors
found that political stability plays an important role in controlling
environmental quality. Similarly, another study [3] added economic
freedom to the energy-growth-environment nexus for 85 developed and
developing countries. The results of FMOLS indicate that the role of
institutions, as well as renewable energy, have a significant positive
impact on economic growth and environmental quality. According to
Adams and Klobodu [31], democracy and bureaucratic quality are the
main determinants of environmental quality.
In a nutshell, the findings on the potential contribution of renewable
energy to environmental quality are not clear. Furthermore, there is
scant literature on the possible impact of institutional stability and
quality in environmental maintenance. The present study fills this gap
by employing the Diet and Rosa environmental function to analyze the
energy-institutional stability-environment nexus.
3. Methods and material
The main objectives of the present study are to analyze both the
renewable energy-institutional stability-economic growth nexus and the
renewable energy-institutional stability-environmental quality nexus of
the D-8 countries. The panel dataset is constructed using the time span
from 1990 to 2016 (27 observations per cross-section). To analyze the
economic growth nexus, this study has employed the modified
Cobb–Douglas production function, where the impact of the different
input variables is analyzed on the economic activity of the D-8 countries.
Following previous studies, this one has used nonrenewable energy
consumption in the model as a comparison with renewable energy
consumption. Furthermore, we cannot exclude conventional energy
sources from the model to avoid the bias and error of omitted variables,
as previous studies have shown a significant association of fossil fuel
energy with economic output and environmental quality.
The first model of the study pertains to capital, labor, technology as a
traditional factor of production, along with renewable and nonrenew
able energy consumption and democracy index (institutional stability).
The initial production function is
Y ¼ fðKLTÞ
The modified production function for the study is
Y ¼ fðK L IS REC NRECÞ
After adding parameters, the equation form is
Y ¼ α þ β1ðKitÞ þ B2ðLitÞ þ β3ðNRECitÞ þ β4ðRECitÞ þ B5ðISitÞ þ ε (1)
Table 1 contains details of the variables along with measurement
units and data sources. The data is measured in different units, which
can cause problems associated with the distributional properties of the
data series [13], so, following the previous literature, all the variables
are transformed into a natural logarithm. The log-linear production
function is
ln Y ¼ α þ β1ðlnKitÞ þ B2ðlnLitÞ þ β3ðlnNRECitÞ þ β4ðRlnRECitÞ þ B5ðInISitÞ
þ ε
(2)
To analyze the renewable-institutional stability-environmental
quality nexus, Diet and Rosa’s environmental function is adopted. The
model, originally developed by Rosa [29], has been subject to devel
opment and changes, and many authors have added different parame
ters according to their study objectives, along with the basic parameters
e.g. population, per capita income, and affluence technology. In this
study, the model modified by Paramati, Sinha and Dogan [13] is
adopted and a measure of institutional stability is added to the equation.
CO2E ¼ fðPOPG PI IS REC NRECÞ
After adding the parameters, the equation form is
CO2E ¼ α þ β1ðPIitÞ þ β2ðPOPGitÞ þ β3ðNRECitÞ þ β4ðRECitÞ þ β5ðISitÞ þ ε
(3)
To overcome the distribution properties of data series, all variables
are transformed into a natural logarithm. The log-linear equation for the
study is
lnCO2E ¼ α þ β1ðlnPIitÞ þ β2ðlnPOPGitÞ þ β3ðlnNRECitÞ
þ β4ðlnRECitÞ þ β5ðlnISitÞ þ ε
(4)
The study has employed ARDL estimation for co-integration as it has
many advantages over other co-integration techniques. ARDL estima
tion gives rigorous results for a small sample and allows multiple pre
dictors to have different lag orders [12]. Based on ARDL estimation, the
empirical specification of equations (3) and (4) is:
where lnCO2E, ln, lnPI, lnPOPG, lnK, lnL, lnNREC, lnREC, lnIS indicate
natural log forms of carbon dioxide emission, economic growth, per
capita income, population growth, capital formation, labor employed,
nonrenewable energy consumption, renewable energy consumption,
and institutional stability measured as the average of civil liberty and
ΔlnYt ¼ α0 þ δ1Yt À 1 þ δ2 ln KtÀ 1 þ δ3 ln LtÀ 1 þ δ4 ln NRECtÀ 1 þ δ5 ln RECtÀ 1 þ δ6 ln IStÀ 1þ
Xp
i¼1
β1jΔY À i þ
Xp
i¼0
β2jΔlnKt À i þ
Xp
i¼0
β3jΔlnLt À i þ
Xp
i¼0
β4jΔlnNRECt À i þ
Xp
i¼0
β5jΔlnRECt À i þ
Xp
i¼0
β6jΔlnISt À i þ εt
(5)
ΔlnCO2t ¼ α0 þ δ1CO2t À 1 þ δ2 ln PItÀ 1 þ δ3 ln POPGtÀ 1 þ δ4 ln NRECtÀ 1 þ δ5 ln RECtÀ 1 þ δ6 ln ISt À 1þ
Xp
i¼1
β1jΔCO2t À i þ
Xp
i¼0
β2jΔlnPIt À 1 þ
Xp
i¼0
β3jΔlnPOPGt À 1 þ
Xp
i¼0
β4jΔlnNRECt À 1 þ
Xp
i¼0
β5jΔlnRECt À 1 þ
Xp
i¼0
β6jΔlnISt À 1 þ εt
(6)
Mahjabeen et al.

6
political rights [26] respectively. α denotes the intercept, Δ is the first
difference operator, p is the lag order, β and δ denote the slope coeffi
cient, ε is a stochastic term.
4. Results and discussion
The empirical testing of the study begins with descriptive statistics
and the correlation matrix. Table 2 presents the correlation matrix and
the descriptive statistics of all the variables. The descriptive statistics
indicate that there are no outliers in the data and the data is stable. The
correlation matrix indicates that some independent variables have a
high correlation but, in the panel data analysis, the number of obser
vations is greater than the time series analysis, which reduces the
concern of multicollinearity [49]. There is a strong and positive corre
lation (0.859081) between GDP and renewable energy consumption; on
the other hand, the correlation between nonrenewable energy con
sumption and GDP is À 0.35996. For carbon emissions, renewable en
ergy has a negative (À 0.43778) correlation with environmental
degradation, indicating the opposite direction of variables, whereas
nonrenewable energy has a positive correlation (0.634464).
The examination of the order of the variables is the most important
step before applying any other econometric tools because it determines
the selection of models for empirical analysis. To estimate the distri
butional properties of the variables, four different panel unit root tests
are examined, where the Lavin-Lin-Chu (LLC) test is used to examine the
common unit test while the individual unit-roots are analyzed by the
Augmented Dicky-Fuller (ADF), Im-Pesaran-Shin (IPS) and Philips-
Perron(PP) tets. The analysis of the integration and application of
these tests is very important, e.g. if results confirm the stationary at I (1),
they indicate that all the variables are stationary at first difference or
first-order differentials and not stationary at a level so the variables
might cointegrate as a group in the long run. Table 3 presents the results
of the unit root test that indicates some of the variables are stationary at
the level and others are stationary at first difference. Institutional sta
bility and nonrenewable energy are stationary at a level while the rest of
the variables are stationary at first difference. A decision was made
regarding the significance of majority results, i.e., if three out of four
tests indicate stationarity at the level, then the variable would be
considered as stationary at level. The same is the case for the first dif
ference. It is important to note that none of the variables are stationary
at second difference 1(2), which leads to the estimation of the ARDL
cointegration bound testing.
The autoregressive distributed lag (ARDL) approach of cointegration
developed by Ref. [50] is employed in this study, as this econometric
tool is widely used [13,27,28] and has many advantages over other
cointegration tools, e.g. Johansen and Juselius. Other cointegration
tools use more than one equation in establishing long-term parameters
while ARDL uses one equation. This approach can also be used whether
the variables are stationary at the level or at first-order [26]. It also
avoids the endogeneity problem and serial correlation and, finally, it is
most suitable for small samples. The ARDL test was followed by a Wald
test to determine the robustness of the estimates through the F-statistics.
The null hypothesis for the Wald test assumes no cointegration among
the parameters (β1 ¼ β2 ¼ β3 ¼ β4 ¼ β5 ¼ 0) while the alternative
hypothesis assumes cointegration among variables (β1 6¼ β2 6¼ β3 6¼ β4 6¼
β5 6¼ 0). The short-term results of the ARDL tests are presented in Table 4
for both models. The Akaike info criterion (AIC) is selected with a
“constant trend” option, while the optimal lag length for economic
output function is one and, for the environment function, the lag length
is 2. The results for the environmental function indicate that all the
variables have a significant association with their dependent variables;
population growth contributes negatively to environmental degradation
so that a 1% increase in population results in a 1.445% decrease in
environmental quality. Particularly, renewable energy has a negative
Table 2
Descriptive statistics and correlation matrix of D-8 countries.
MEAN SD LNIS LNREC LNNREC LNPOP LNPI LNK LNL LNGDP LNCO2E
LNIS 1.49228 0.25722 1
LNREC 8.85905 1.50054 À 0.44668 1
LNNREC 4.15913 0.57359 0.08275 À 0.68494 1
LNPOPG 0.63319 0.34364 0.13043 0.22070 À 0.69633 1
LNPI 7.63854 1.04561 À 0.05602 0.18233 À 0.04892 0.05938 1
LNK 3.03774 0.38800 À 0.10176 À 0.32104 0.73951 À 0.68791 0.00282 1
LNL 3.79550 0.34795 À 0.26260 À 0.31193 0.71621 À 0.61083 À 0.33963 0.66085 1
LNGDP 25.67461 1.51152 À 0.10897 À 0.35996 0.85908 À 0.83766 0.02098 0.72596 0.74674 1
LNCO2E 0.45466 1.10431 0.22101 À 0.43778 0.63446 À 0.40407 0.60558 0.49079 0.22797 0.61612 1
Table 3
Testing the stationarity of the variables.
Variables LLC IPS ADF PP Decision
1(0) 1(1) 1(0) 1(1) 1(0) 1(1) 1(0) 1(1)
LNCO2E À 2.1798
(0.0146)
À 6.5124
0.0000
0.26136
(0.6031)
6.77555
0.0000
11.6462
À 0.7680
75.0730
0.0000
13.0309
À 0.6705
151.610
0.0000
1(1)
LNGDP 2.42662 (0.9924 À 2.33925
(0.0097)
5.72021
(1.0000)
4.79507
(0.0000)
2.41804
(1.0000)
54.2891
(0.0000)
6.02684
(0.9878)
102.699
(0.0000)
1(1)
LNIS À 1.0159
(0.1548)
À 5.44871
(0.0000)
À 2.14484
(0.0160)
7.07762
(0.0000)
29.3938
À 0.0214
78.7041
(0.0000)
30.3527
(0.0163)
119.682
(0.0000)
1(0)
LNNREC À 4.0739
0.0000
À 6.2060
(0.0000)
À 1.9925
À 0.0232
À 6.1070
(0.0000)
29.9875
À 0.0181
66.7509
(0.0000)
45.8559
(0.0001)
45.8559
(0.0001)
1(0)
LNREC À 0.8871
À 0.1875
À 5.4370
0.0000
1.8666
À 0.9690
À 5.5890
0.0000
7.4383
(0.9639)
61.0456
(0.0000)
7.1538
(0.9702)
109.5280
(0.0000)
1(1)
LNK À 1.72404
(0.0424)
À 6.6695
(0.0000)
À 0.5293
(0.2983)
À 6.4174
(0.0000)
14.1433
À 0.5880
70.5948
(0.0000)
16.1385
(0.4433)
91.9635
(0.0000)
1(1)
LNL 3.9568
À 1.0000
9.1130
À 1.0000
0.62870
(0.7352)
À 3.7782
À 0.0001
11.5108
(0.7769)
44.7561
À 0.0002
24.4403
À 0.0803
85.2033
0.0000
1(1)
LNPI 0.61523
(À 0.7308)
À 4.4405
(0.0000)
2.76075
(À 0.9971)
À 5.1930
(0.0000)
4.66816
(0.9972)
56.9754
(0.0000)
3.1595
(0.9998)
90.0874
(0.0000)
1(1)
LNPOPG 0.32566
(0.6277)
À 3.0715
À 0.0011
0.60691
À 0.7280
À 5.2828
(0.0000)
14.2645
À 0.5790
63.7080
0.0000
39.9819
(0.0008)
22.1500
(0.1384)
1(1)
Mahjabeen et al.

7
association with CO2 emissions, which has important implications for
policymakers. Interestingly, nonrenewable energy consumption has an
inverse relationship with CO2 emissions; on the other hand, per capita
income and democracy index have positive associations. All parameters
have a positive association with economic output, which implies the
positive contribution of disaggregated energy sources and other pro
duction factors. The institutional stability measure, however, shows a
significant but negative correlation with economic activity. Previously,
Bulut and Muratoglu [26] found this variable had a negative but insig
nificant impact on Turkish economic activity. We can interpret this
finding as a lower institutional stability index showing high stability, so
high stability has a positive relationship with economic activity. The
Wald test statistics showed a significant probability (0.000) F-statistics
(134.6705, 384.6973) that showed the robustness of the model and the
long-term associations among variables. These estimates led the authors
to test long-term elasticities.
After determining the co-integration among variables, the long-run
elasticities or parameters can be determined through ordinary least
square (OLS), dynamic ordinary least square (DOLS) and fully modified
ordinary least square (FMOLS) tests developed by Pedroni [51]. The
estimated cointegrated vector through OLS is super convergent with the
asymptotically biased distribution. These problems are similar for time
series as well as for panel data and more salient even in the presence of
heterogeneity [39]. Using the semiparametric corrections, FMOLS ac
counts for serial correlation and endogeneity in errors; on the other
hand, DOLS uses the generalized least square procedure to correct the
serial correlation in errors and leads and lags to correct the endogeneity
in regressors [14]. So, the study has employed both estimators for
robustness checks to estimate the long-term elasticities of variables, as
both techniques correct the endogeneity and serial correlation problems.
Table 5 presents the results of DOLS and FMOLS estimates. Results of
FMOLS indicate that the institutional stability index has a negative and
significant association with economic output; we can interpret this as a
low institutional stability index indicating high stability (1 ¼ most
stable, 7 ¼ least), that is why increased political stability will create
increased economic output. So institutional stability plays a vital role in
establishing the economy on a healthy footing. On the other hand, the
DOLS results show a negative but insignificant association of institu
tional stability with economic output as found by Bulut and Muratoglu
[26] in their DOLS estimation of democracy in a regional study of
Turkey. Both capital and labor are significant contributors to economic
output in both methods. The important parameters of economic output,
renewable and non-renewable energy consumption contribute posi
tively to economic development. So, both types of energy are essential
for economic growth. This finding has a significant policy implication as
it appeals to a blend of both types of energy and recommendations of a
gradual transition to an eco-friendly energy system.
Coming to the environment function, the FMOLS results indicate that
the institutional stability index has a significant association with the
environment, while it is insignificant in the DOLS estimation. Focusing
on FMOLS estimation, we can infer that institutional stability and
environmental degradation have a negative and significant association,
as institutional stability will decrease environmental degradation by
0.31%. Population growth does not show a significant association while
per capita income has a significant and positive impact on carbon
emissions, which implies an increase in income spurs CO2 emissions. We
can presume therefore that the people of this region are not environ
mentally conscious yet, as they pollute their environment more as their
level of income increases, so the government needs to establish a proper
awareness campaign to educate the public about the importance of a
clean environment.
Both renewable and non-renewable energy consumption have sig
nificant links with CO2 emissions as predicted in FMOLS, but the results
reveal that the beta coefficient of renewable energy consumption is
much less than that of nonrenewable energy consumption, which im
plies that renewable energy consumption has a less damaging impact on
the environment than nonrenewable energy consumption. We can
further argue that renewable energy consumption needs technological
involvement and proper planning of capital resources, but these coun
tries are lacking in these fields, which is why renewable energy con
sumption is adding to the environmental pollution to some extent.
Focusing on institutional quality, we can further argue that governments
need to focus on technical aspects, clean production and consumption of
energy, as most of the sources of renewable energy production are
biogases and animal dungs that also have implications for the environ
ment. So curtailing conventional sources and increasing clean produc
tion of renewable energy with greater emphasis on photovoltaic energy
are recommended to grasp this opportunity.
The study proposed causality between independent variables and
dependent variables for both of the equations. To analyze whether the
causality is bidirectional, one way or if there is no causal association, the
Granger causality approach is employed. The results presented in
Table 6 indicate that there is no bidirectional causality between vari
ables. As far as unidirectional causality is concerned, institutional sta
bility Granger causes carbon emissions and renewable energy
Table 4
Short term estimate of ARDL test.
CO2E ¼ fðPOPG PI DI REC NRECÞ Y ¼ fðK L DI REC NRECÞ
Variables Coefficient T-Stat Sig Variables Coefficient T-Stat Sig
LNPOPG À 1.445118 À 5.21328 0.0000 LNIS À 0.232521 À 3.257713 0.0014
LNIS 0.253302 2.61238 0.0103 LNK 0.280819 3.174422 0.0018
LNNREC À 2.761693 À 3.48599 0.0007 LNL 0.686186 2.639437 0.0092
LNPI 0.645139 8.58696 0.0000 LNNREC 7.092142 13.64693 0.0000
LNREC À 0.590357 À 2.26751 0.0254 LNREC 1.207882 11.85181 0.0000
AIC ¼ À 2.717660, Schwarz criterion ¼ À 1.01439 AIC ¼ À 4.38704, Schwarz criterion ¼ À 3.433836
Wald test statistics Wald test statistics
F-statistic ¼ 134.6705, p value ¼ 0.0000 F-statistic ¼ 384.6973, p value ¼ 0.0000
Chi-square ¼ 673.3525, p value ¼ 0.0000 Chi-square ¼ 1923.487, p value ¼ 0.0000
Table 5
DOLS and FMOLS estimates of long-term elasticities.
Y ¼ fðK L IS REC NRECÞ CO2E ¼ fðPOPG PI IS REC NRECÞ
Variables Coefficient Prob Variables Coefficient Prob
FMOLS estimates
LNIS À 0.13431 0.0352 LNIS À 0.31502 0.002
LNK 0.140893 0.0014 LNPOPG À 0.12485 0.6634
LNL À 0.5658 0.0504 LNPI 0.133255 0.0129
LNNREC 10.06462 0.0000 LNREC 0.575134 0.0011
LNREC 1.149324 0.0000 LNNREC 5.58897 0.0000
DOLS estimates
LNIS À 0.4177 0.0783 LNIS À 0.12213 0.6033
LNK 0.372146 0.0067 LNPOPG À 0.54772 0.1488
LNL À 1.94931 0.0012 LNPI 0.309957 0.0065
LNNREC 13.19915 0.0000 LNREC 0.194662 0.2329
LNREC 1.24247 0.0000 LNNREC À 2.03874 0.0352
Mahjabeen et al.

8
consumption Granger causes institutional stability. For the second
model, GDP and institutional stability Granger causes capital formation,
labor force Granger causes GDP, while renewable energy Granger causes
the capital formation and capital formation Granger causes nonrenew
able energy. The study neither found any causality between energy
consumption and economic growth nor with carbon emissions, hence
the results support the neutrality hypothesis. The findings of the
neutrality hypothesis are in line with previous findings where the au
thors have found no causal link [26], despite finding short and long-term
associations among variables.
5. Conclusion
Energy has become a strategic commodity without which the func
tioning of the economy is threatened, especially in developing countries.
Developing countries are facing the two-sided sword of energy chal
lenges: On one hand, they have to overcome energy shortages and meet
energy demand, while, on the other hand, environmental degradation
poses a serious threat to sustainable development. That is why efforts are
being made to spread the awareness of the utility and environmental
compatibility of renewable energy. This study seeks to analyze the
impact of renewable and nonrenewable energy consumption and insti
tutional stability, along with other factors, on the environment and
economic output or activity. The analysis is divided into two models; the
first model pertains to the energy-institutional stability-economic
growth nexus that incorporates the modified Cobb–Douglas production
function. The second model pertains to the energy-institutional stability-
environment quality nexus that incorporates the Diet and Rosa envi
ronmental function. D-8 countries were selected for analysis, as they
mostly depend on conventional energy sources and suffer from low
institutional quality. The data time period spans from 1990 to 2016 and
panel data methodology is adopted for analysis. The authors applied
different econometric techniques for the statistical analysis. The unit
root test indicates that some of the variables are stationary at a level and
some variables are stationary at first difference; that is why the ARDL
bounds testing approach was incorporated to analyze the cointegration
among variables. The results of both the equations indicate significant
cointegration for the constituents of both functions. Further, the long-
term elasticities are measured through FMOLS and DOLS estimation,
where results indicate that both renewable and non-renewable energy
has a strong association with economic development, as well as
contributing to carbon emissions. However, the negative effect of
renewable energy is much less than of nonrenewable energy. The vari
able of institutional stability positively impacts economic growth and
environmental quality. The Granger causality results of the study sup
port the neutrality hypothesis, as no causality is found in economic
output and energy consumption.
Although the study has incorporated different indicators and mea
sures, it is not beyond certain limitations. First, the study has employed
important input factors for both of the functions but authors suggest
incorporating some other resources as an input factor for environmental
quality and economic growth, e.g. water resources, natural resource
depletion. Second, the financial and economic integrations of economies
and the mutual interplay of resources leads to common shocks and
dependence of economies on each other that is termed “cross-sectional
dependence”. The study has employed first-generation techniques of
panel data analysis; ARDL, FMOLS and DOLS do not allow the consid
eration of cross-sectional dependence, the authors suggest incorporating
the second-generation techniques of panel data analysis that addresses
the cross-sectional dependence issue more accurately, e.g. dynamic
common correlated method, common correlated effect mean group
estimators.
5.1. Policy implications
Institutional stability is the basic parameter without which sustain
able development cannot be imagined. The governments of these
countries need to focus on cleaner energy production and the specific
agenda of environmental protection to achieve sustainable development
goals. Other suitable policy suggestions are:
Table 6
Granger causality test.
Variables Links F-statistics (p-value) Decision Variables Links F-statistics (p-value) Decision
LNIS toward LNCO2E 8.90070 (0.0002) LNIS →LN CO2E LNIS towards LNGDP 2.48580 (0.0859) No causality
LNCCO2E toward LNIS 1.23828 (0.2922) LNGDP towards LNIS 0.13878 (0.8705)
LNPOPG towards LN CO2E 1.72636 (0.1806) No causality LNK towards LNGDP 0.31723 (0.7285) LNGDP→LNK
LNCO2E towards LNPOPG 0.15360 (0.8577) LNGDP towards LNK 4.03479 (0.00192)
LNPI towards LN CO2E 2.21398 (0.1120) No causality LNL towards LNGDP 5.03387(0.0074) LNL →LNGDP
LNCO2E towards LNPI 1.79590 (0.1687) LNGDP towards LNL 0.12130 (0.8858)
LNREC towards LN CO2E 0.96264 (0.3837) No causality LNNREC towards LNGDP 0.66090 (0.5175) No causality
LNCO2E towards LNREC 0.21365(0.8078) LNGDP towards LNNREC 0.01937 (0.9808)
LNNREC towards LN CO2E 2.88959 (0.0580) No causality LNREC towards LNGDP 1.59010 (0.2065) No causality
LNCO2E towards LNNREC 2.16708 (0.1173) LNGDP towards LNREC 0.21113 (0.8099)
LNPOPG towards LNIS 0.83291 (0.4363) No causality LNK towards LNIS 0.45486 (0.6352) LNIS →LNK
LNIS towards LNPOPG 0.58182 (0.5598) LNIS towards LNK 3.46012 (0.0334)
LNPI towards LNIS 1.74885(0.1767) No causality LNL towards LNIS 0.32191 (0.7252) No causality
LNIS towards LNPI 1.40277(0.2484) LNIS towards LNL 0.33057 (0.7189)
LNREC towards LNIS 4.09167 (0.0182) LNREC → LNIS LNNREC towards LNIS 0.31186 (0.7324) No causality
LNIS towards LNREC 0.00250 (0.9975) LNIS towards LNNREC 0.02067 (0.9795)
LNNREC towards LNIS 0.31186 (0.7324) No causality LNREC towards LNIS 4.09167 (0.0182) LNREC→LNIS
LNIS towards LNNREC 0.02067 (0.9795) LNIS towards LNREC 0.00250 (0.9975)
LNPI towards LNPOPG 0.36700 (0.6933) No causality LNL towards LNK 1.68114 (0.1888) No causality
LNPOPG towards LNPI 1.91489 (0.1501) LNK towards LNL 0.98418 (0.3756)
LNREC towards LNPOPG 0.06775 (0.9345) No causality LNNREC towards LNK 1.99022 (0.2394) LNK→LNNREC
LNPOPG towards LNREC 1.10422 (0.3335) LNK towards LNNREC 8.11877 (0.0004)
LNNREC towards LNPOPG 2.13534 (0.1210) No causality LNREC towards LNK 0.08201 (0.9213) No causality
LNPOPG towards LNNREC 0.21192 (0.8092) LNK towards LNREC 0.09283 (0.9114)
LNREC towards LNPI 0.36036(0.6970) No causality LNNREC towards LNL 0.22837 (0.796) No causality
LNPI towards LNREC 0.51532 (0.5981) LNL towards LNNREC 0.65823 (0.5189)
LNNREC towards LNPI 0.10324 (0.9020) No causality LNREC towards LNL 0.27677 (0.7585) No causality
LNPI towards LNNREC 0.43092 (0.6505 LNL towards LNREC 0.34808 (0.7065)
LNNREC towards LNREC 0.03960 (0.9612) No causality LNREC towards LNNREC 0.06929 (0.9331) No causality
LNREC towards LNNREC 0.06929 (0.9331) LNNREC towards LNREC 0.03960 (0.9612)
Mahjabeen et al.

9
➢ These countries are naturally blessed with different renewable en
ergy sources due to their geographical location. But the statistics
show that hydropower is the major source of renewable energy in
these countries. So, the governments should not remain limited to
this source and must invest in other sources to capitalize on other
opportunities. Further, the governments should subsidize renewable
energy consumption and should also maintain some other incentives,
especially for rural electrification through renewable energy sources.
➢ Two of the D-8 member countries, Pakistan and Iran, have the ability
to produce energy through nuclear technology. Therefore, these
countries can enhance the production of renewable energy through
nuclear technology for local use as well as for trade purposes. D-8
countries have various contracts along with efficient energy provi
sion, so these countries can come up with effective nuclear as well as
other energy trade agreements.
➢ Imposing a carbon tax on the excessive use of conventional energy
sources and using that fund for proper procurement of renewable
energy will eventually enable renewable energy to compete with
conventional sources. So the governments are recommended to
ensure the proper implementation of policies to abate the environ
mental consequences of conventional energy sources.
➢ Last but not least, a public awareness campaign, to spread the
knowledge about the usability of renewable energy and anthropo
genic implications of conventional energy, should be scheduled
through proper training and programs. The governments of these
countries are encouraged to arrange such a media campaign to urge
and educate the public to participate in making the world a better
place to live in for themselves and future generations.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
CRediT authorship contribution statement
Mahjabeen: Conceptualization, Writing - original draft, Data cura
tion. Syed Z.A. Shah: Conceptualization, Supervision, Validation.
Sumayya Chughtai: Methodology, Software, Writing - review & edit
ing. Biagio Simonetti: Formal analysis.
References
[1] S. Sen, S. Ganguly, Opportunities, barriers and issues with renewable energy
development – a discussion, Renew. Sustain. Energy Rev. 69 (2017) 1170–1181,
https://doi.org/10.1016/j.rser.2016.09.137.
[2] T.W. Brown, T. Bischof-Niemz, K. Blok, C. Breyer, H. Lund, B.V. Mathiesen,
Response to ‘Burden of proof: a comprehensive review of the feasibility of 100%
renewable-electricity systems,’, Renew. Sustain. Energy Rev. 92 (2018) 834–847,
[3] M. Bhattacharya, S. Awaworyi Churchill, S.R. Paramati, The dynamic impact of
renewable energy and institutions on economic output and CO 2 emissions across
regions, Renew. Energy 111 (2017) 157–167, https://doi.org/10.1016/j.
renene.2017.03.102.
[4] A.A.F. Husain, W.Z.W. Hasan, S. Shafie, M.N. Hamidon, S.S. Pandey, A review of
transparent solar photovoltaic technologies, Renew. Sustain. Energy Rev. 94
(2018) 779–791, https://doi.org/10.1016/j.rser.2018.06.031.
[5] M. Ahmad, G. Jabeen, M. Irfan, M. Claire, M. Naseer, A. Maria, Modeling causal
interactions between energy investment , pollutant emissions , and economic
Growth : China study, Biophys. Econ. Sustain. 7 (2020) 1–12, https://doi.org/
10.1007/s41247-019-0066-7.
[6] B.M. Eren, N. Taspinar, K.K. Gokmenoglu, The impact of financial development
and economic growth on renewable energy consumption: empirical analysis of
India, Sci. Total Environ. 663 (2019) 189–197, https://doi.org/10.1016/j.
scitotenv.2019.01.323.
[7] M. Kahia, M.S. Ben Aïssa, C. Lanouar, Renewable and non-renewable energy use -
economic growth nexus: the case of MENA Net Oil Importing Countries, Renew.
Sustain. Energy Rev. 71 (2017) 127–140, https://doi.org/10.1016/j.
rser.2017.01.010.
[8] W.U.K. Tareen, Z. Anjum, N. Yasin, L. Siddiqui, I. Farhat, S.A. Malik, S. Mekhilef,
M. Seyedmahmoudian, B. Horan, M. Darwish, M. Aamir, L.W. Chek, The
prospective non-conventional alternate and renewable energy sources in Pakistan -
a focus on biomass energy for power generation, transportation, and industrial
fuel, Energies 11 (2018), https://doi.org/10.3390/en11092431.
[9] D. Gielen, F. Boshell, D. Saygin, M.D. Bazilian, N. Wagner, R. Gorini, The role of
renewable energy in the global energy transformation, Energy Strateg. Rev. 24
(2019) 38–50, https://doi.org/10.1016/j.esr.2019.01.006.
[10] M. Kamran, Current status and future success of renewable energy in Pakistan,
Renew. Sustain. Energy Rev. 82 (2018) 609–617, https://doi.org/10.1016/j.
rser.2017.09.049.
[11] K. Dong, G. Hochman, Y. Zhang, R. Sun, H. Li, H. Liao, CO 2 emissions, economic
and population growth, and renewable energy: empirical evidence across regions,
Energy Econ. 75 (2018) 180–192, https://doi.org/10.1016/j.eneco.2018.08.017.
[12] S.A. Sarkodie, S. Adams, Renewable energy, nuclear energy, and environmental
pollution: accounting for political institutional quality in South Africa, Sci. Total
Environ. 643 (2018) 1590–1601, https://doi.org/10.1016/j.
scitotenv.2018.06.320.
[13] S.R. Paramati, A. Sinha, E. Dogan, The significance of renewable energy use for
economic output and environmental protection: evidence from the Next 11
developing economies, Environ. Sci. Pollut. Res. 24 (2017) 13546–13560, https://
doi.org/10.1007/s11356-017-8985-6.
[14] S. Adams, E.K.M. Klobodu, A. Apio, Renewable and non-renewable energy, regime
type and economic growth, Renew. Energy 125 (2018) 755–767, https://doi.org/
10.1016/j.renene.2018.02.135.
[15] N. Apergis, J.E. Payne, Renewable energy consumption and growth in Eurasia,
Energy Econ. 32 (2010) 1392–1397, https://doi.org/10.1016/j.
eneco.2010.06.001.
[16] S. Farhani, M. Shahbaz, What role of renewable and non-renewable electricity
consumption and output is needed to initially mitigate CO2 emissions in MENA
region? Renew. Sustain. Energy Rev. 40 (2014) 80–90, https://doi.org/10.1016/j.
rser.2014.07.170.
[17] M. Mert, G. B€olük, Do foreign direct investment and renewable energy
consumption affect the CO2emissions? New evidence from a panel ARDL approach
to Kyoto Annex countries, Environ. Sci. Pollut. Res. 23 (2016) 21669–21681,
https://doi.org/10.1007/s11356-016-7413-7.
[18] F. Bilgili, E. Koçak, Ü. Bulut, The dynamic impact of renewable energy
consumption on CO 2 emissions: a revisited Environmental Kuznets Curve
approach, Renew. Sustain. Energy Rev. 54 (2016) 838–845, https://doi.org/
10.1016/j.rser.2015.10.080.
[19] G. Heal, Reflections-the economics of renewable energy in the United States, Rev.
Environ. Econ. Pol. 4 (2009) 139–154, https://doi.org/10.1093/reep/rep018.
[20] F.M. Mirza, A. Kanwal, Energy consumption, carbon emissions and economic
growth in Pakistan: dynamic causality analysis, Renew. Sustain. Energy Rev. 72
(2017) 1233–1240, https://doi.org/10.1016/j.rser.2016.10.081.
[21] U.K. Mirza, N. Ahmad, K. Harijan, T. Majeed, Identifying and addressing barriers to
renewable energy development in Pakistan, Renew. Sustain. Energy Rev. 13 (2009)
927–931, https://doi.org/10.1016/j.rser.2007.11.006.
[22] M.K. Shahzad, A. Zahid, T. Rashid, M.A. Rehan, M. Ali, M. Ahmad, Techno-
economic feasibility analysis of a solar-biomass off grid system for the
electrification of remote rural areas in Pakistan using HOMER software, Renew.
Energy 106 (2017) 264–273, https://doi.org/10.1016/j.renene.2017.01.033.
[23] R. Bellakhal, S. Ben Kheder, H. Haffoudhi, Governance and renewable energy
investment in MENA countries:How does trade matter? Energy Econ. 84 (2019)
104541, https://doi.org/10.1016/j.eneco.2019.104541.
[24] S. Adams, E.K.M. Klobodu, E.E.O. Opoku, Energy consumption, political regime
and economic growth in sub-Saharan Africa, Energy Pol. 96 (2016) 36–44, https://
doi.org/10.1016/j.enpol.2016.05.029.
[25] S. Adams, P.K. Adom, E.K.M. Klobodu, Urbanization, regime type and durability,
and environmental degradation in Ghana, Environ. Sci. Pollut. Res. 23 (2016)
23825–23839, https://doi.org/10.1007/s11356-016-7513-4.
[26] U. Bulut, G. Muratoglu, Renewable energy in Turkey: great potential, low but
increasing utilization, and an empirical analysis on renewable energy-growth
nexus, Energy Pol. 123 (2018) 240–250, https://doi.org/10.1016/j.
enpol.2018.08.057.
[27] O. Ocal, A. Aslan, Renewable energy consumption-economic growth nexus in
Turkey, Renew. Sustain. Energy Rev. 28 (2013) 494–499, https://doi.org/
10.1016/j.rser.2013.08.036.
[28] M. Shahbaz, N. Loganathan, M. Zeshan, K. Zaman, Does renewable energy
consumption add in economic growth? An application of auto-regressive
distributed lag model in Pakistan, Renew. Sustain. Energy Rev. 44 (2015) 576–585,
[29] E.U.A.R. Osa, Effects of population and affluence on CO 2 emissions, Ecology 94
(1997) 175–179.
[30] I.M.A. Bengochea-morancho, R. Morales-lage, The Impact of Population on CO 2
Emissions : Evidence from European Countries, Foxit Reader, 2007, pp. 497–512,
https://doi.org/10.1007/s10640-007-9096-5.
[31] S. Adams, E.K.M. Klobodu, Urbanization, democracy, bureaucratic quality, and
environmental degradation, J. Pol. Model. 39 (2017) 1035–1051, https://doi.org/
10.1016/j.jpolmod.2017.04.006.
[32] E. Productivity, D -8 Economic Outlook 2016/2017, 2017.
[33] A. Kraft, John kraft, on the relationship between energy and GNP author ( s ): John
Kraft and Arthur Kraft stable, J. Energy Dev. 3 (1978) 401–403. On the
Relationship Between Energy and GNP, http://www.jstor.org/stable/24806805.
[34] T. Chien, J.L. Hu, Renewable energy and macroeconomic efficiency of OECD and
non-OECD economies, Energy Pol. 35 (2007) 3606–3615, https://doi.org/
10.1016/j.enpol.2006.12.033.
Mahjabeen et al.

10
[35] S.S. Amjid, M.Q. Bilal, M.S. Nazir, A. Hussain, Biogas, renewable energy resource
for Pakistan, Renew. Sustain. Energy Rev. 15 (2011) 2833–2837, https://doi.org/
10.1016/j.rser.2011.02.041.
[36] X.C. Yuan, Y.J. Lyu, B. Wang, Q.H. Liu, Q. Wu, China’s energy transition strategy at
the city level: the role of renewable energy, J. Clean. Prod. 205 (2018) 980–986,
https://doi.org/10.1016/j.jclepro.2018.09.162.
[37] P. Sadorsky, Renewable energy consumption and income in emerging economies,
Energy Pol. 37 (2009) 4021–4028, https://doi.org/10.1016/j.enpol.2009.05.003.
[38] N. Apergis, J.E. Payne, The causal dynamics between renewable energy, real GDP,
emissions and oil prices: evidence from OECD countries, Appl. Econ. 46 (2014)
4519–4525, https://doi.org/10.1080/00036846.2014.964834.
[39] S. Farhani, Renewable energy consumption, economic growth and CO2 emissions:
evidence from selected MENA countries, Energy Econ. Lett. 1 (2013) 24–41,
https://doi.org/10.1109/GLOCOM.2010.5683968.
[40] M. Sebri, O. Ben-Salha, On the causal dynamics between economic growth,
renewable energy consumption, CO2emissions and trade openness: fresh evidence
from BRICS countries, Renew. Sustain. Energy Rev. 39 (2014) 14–23, https://doi.
org/10.1016/j.rser.2014.07.033.
[41] Danish, B. Zhang, B. Wang, Z. Wang, Role of renewable energy and non-renewable
energy consumption on EKC: evidence from Pakistan, J. Clean. Prod. 156 (2017)
855–864, https://doi.org/10.1016/j.jclepro.2017.03.203.
[42] R. Zeb, L. Salar, U. Awan, K. Zaman, M. Shahbaz, Causal links between renewable
energy, environmental degradation and economic growth in selected SAARC
countries: progress towards green economy, Renew. Energy 71 (2014) 123–132,
https://doi.org/10.1016/j.renene.2014.05.012.
[43] F.V. Bekun, A.A. Alola, S.A. Sarkodie, Toward a sustainable environment: nexus
between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-
EU countries, Sci. Total Environ. 657 (2019) 1023–1029, https://doi.org/10.1016/
j.scitotenv.2018.12.104.
[44] K. Dong, R. Sun, G. Hochman, Do natural gas and renewable energy consumption
lead to less CO2 emission? Empirical evidence from a panel of BRICS countries,
Energy 141 (2017) 1466–1478, https://doi.org/10.1016/j.energy.2017.11.092.
[45] K. Dong, X. Dong, C. Dong, Determinants of the global and regional CO 2
emissions: what causes what and where? Appl. Econ. (2019) 1–14, https://doi.org/
10.1080/00036846.2019.1606410, 00.
[46] S. Saint Akadiri, F.V. Bekun, S.A. Sarkodie, Contemporaneous interaction between
energy consumption, economic growth and environmental sustainability in South
Africa: what drives what? Sci. Total Environ. 686 (2019) 468–475, https://doi.org/
10.1016/j.scitotenv.2019.05.421.
[47] S.A. Sarkodie, V. Strezov, Assessment of contribution of Australia’s energy
production to CO2 emissions and environmental degradation using statistical
dynamic approach, Sci. Total Environ. 639 (2018) 888–899, https://doi.org/
10.1016/j.scitotenv.2018.05.204.
[48] U. Al-Mulali, I. Ozturk, The effect of energy consumption, urbanization, trade
openness, industrial output, and the political stability on the environmental
degradation in the MENA (Middle East and North African) region, Energy 84
(2015) 382–389, https://doi.org/10.1016/j.energy.2015.03.004.
[49] J. Baek, A new look at the FDI-income-energy-environment nexus: dynamic panel
data analysis of ASEAN, Energy Pol. 91 (2016) 22–27, https://doi.org/10.1016/j.
enpol.2015.12.045.
[50] M.H. Pesaran, Y. Shin, R.J. Smith, Bounds testing approaches to the analysis of
level relationships, J. Appl. Econom. 16 (2001) 289–326, https://doi.org/10.1002/
jae.616.
[51] P. Pedroni, Fully modified Ols for heterogeneous cointegrated panels. htt
p://citeseerx.ist.psu.edu/viewdoc/download?doi¼10.1.1.294.1320&rep¼rep1
&type¼pdf, 2000.
Mahjabeen et al.

Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Similar to Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries

Similar to Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries (20)

More from Power System Operation

More from Power System Operation (20)

Recently uploaded

Recently uploaded (20)

Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries